An AI agent uses a model to select steps and tools toward a goal. Action permissions matter beyond document access: a model proposing an action does not authorize the user to perform it.
Putting it into practice
Start with reading and drafting agents, such as contract summaries or checklists. Before ERP or CRM writes, allowlist tools, restrict service accounts and require human approval for external sharing or consequential changes.
| Compare options | Chatbot | AI Agent |
|---|---|---|
| Outcomes to validate | Answer | Workflow |
| Scope | Chat | AI agents |
| Permission | Read only | Access control |
| Human review | Yes, implemented | Yes, implemented |
Public AI varies by provider, plan and contract. Private AI depends on architecture and configuration. Neither fits every task.
Before you begin
- Tool allowlist
- Action rights
- Approval and stop points
Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.
Frequently asked questions
Does data have to leave the network?
It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.
Can an AI answer be trusted immediately?
Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.
How should a project begin?
Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.
References
Public sources explain principles; they do not certify or endorse RAGBOX.
- NIST AI Risk Management Framework
- Microsoft: Retrieval-augmented generation
- OWASP: RAG Security Cheat Sheet
- OWASP: Prompt Injection Prevention
Expert review is required before operational use. This is not case-specific legal or tax advice.